v0.23.4
Ollama v0.23.4 is a release version of Ollama, a tool for running large language models locally. This patch release likely includes bug fixes, performance improvements, and refinements to existing fea
Knowledge catalogue
Ollama v0.23.4 is a release version of Ollama, a tool for running large language models locally. This patch release likely includes bug fixes, performance improvements, and refinements to existing fea
arXiv:2502.11981v3 Announce Type: replace Abstract: Decades of research in machine learning have given us powerful tools for making accurate predictions. But when used in social settings and on human
arXiv:2605.12090v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) models have achieved strong semantic generalization for embodied policy learning, yet they learn reactive observation-to-
arXiv:2605.08517v1 Announce Type: cross Abstract: We describe an approach for estimating the statistical risk of deep networks that contain a mix of learned and known operators. Building on the maxima
arXiv:2605.09061v1 Announce Type: cross Abstract: Accurate and efficient imbalance electricity price forecasting is critical for industrial energy trading systems, especially as battery assets and aut
arXiv:2605.09283v1 Announce Type: new Abstract: The evolution of Large Language Models (LLMs) and the software agents built on them (AI agents) marks a turning point in the transition from a human-cen
arXiv:2605.09032v1 Announce Type: cross Abstract: Reliable short horizon forecasting of solar and wind generation is a structural prerequisite of any modern power system yet most published forecasters
arXiv:2601.12374v2 Announce Type: replace-cross Abstract: Existing approaches to bias evaluation in large language models (LLMs) trade ecological validity for statistical control, relying either on ar
arXiv:2605.10076v1 Announce Type: cross Abstract: Generative (diffusion) priors demonstrate remarkable performance in addressing inverse problems in imaging. Yet, for scientific and medical imaging, i
arXiv:2512.17593v3 Announce Type: replace Abstract: In this paper we consider the limiting case of neural networks (NNs) architectures when the number of neurons in each hidden layer and the number of
arXiv:2605.10654v1 Announce Type: cross Abstract: We consider the active learning problem where the goal is to learn an unknown function with low prediction error under an unknown Boltzmann distributi
arXiv:2605.09707v1 Announce Type: cross Abstract: Training neural networks to satisfy universal constraints over continuous domains poses unique challenges. Common examples include Lyapunov Neural Net
arXiv:2605.08130v1 Announce Type: new Abstract: We present a framework for the simultaneous symbolic recovery of a function and its antiderivative from data. The framework rests on three ideas. First,
arXiv:2605.10555v1 Announce Type: new Abstract: As AI agents transition from research prototypes to enterprise production systems, the tool interfaces they consume remain rooted in human-oriented CRUD
arXiv:2605.08956v1 Announce Type: new Abstract: A growing body of work pursues AI scientists capable of end-to-end autonomous scientific discovery. This position paper argues that although they alread
arXiv:2605.10590v1 Announce Type: cross Abstract: Causal sensitivity analysis aims to provide bounds for causal effect estimates in the presence of unobserved confounding. However, existing methods fo
arXiv:2605.10107v1 Announce Type: new Abstract: Assertion-based Verification (ABV) is essential for ensuring that hardware designs conform to their intended specifications. However, existing automated
arXiv:2605.09659v1 Announce Type: new Abstract: Koopman operator theory provides a powerful framework for representing nonlinear dynamics through a linear operator acting on lifted observables, enabli
arXiv:2605.08679v1 Announce Type: cross Abstract: Graph neural networks (GNNs) aim to learn well-trained representations in a lower-dimension space for downstream tasks while preserving the topologica
arXiv:2605.09644v1 Announce Type: new Abstract: Visual Geometry Grounded Transformer (VGGT) advances 3D reconstruction via scalable Transformer architecture, but the quadratic complexity of global att
arXiv:2605.10845v1 Announce Type: cross Abstract: As global cross-lingual communication intensifies, language barriers in visually rich documents such as PDFs remain a practical bottleneck. Existing d
arXiv:2605.08110v1 Announce Type: cross Abstract: Low-Rank Adaptation (LoRA) has become the standard for fine-tuning large pre-trained models at reduced computational cost. However, its low-rank point
arXiv:2407.12173v2 Announce Type: replace-cross Abstract: Generative diffusion models have emerged as a powerful tool for high-quality image synthesis, yet their iterative nature demands significant c
arXiv:2603.04783v2 Announce Type: replace Abstract: While LLMs demonstrate strong reasoning capabilities when provided with full information in a single turn, they exhibit substantial vulnerability in
arXiv:2605.09279v1 Announce Type: cross Abstract: Volumetric video (VV) streaming enables real-time, immersive access to remote 3D environments, powering telepresence, ecological monitoring, and robot
arXiv:2605.08439v1 Announce Type: new Abstract: Accurately communicating the side effects of cancer treatments to cancer survivors is critical, particularly in settings such as informed consent, where
arXiv:2605.09092v1 Announce Type: new Abstract: This study addresses automatic transliteration from Tajik (Cyrillic script) to Persian (Perso-Arabic script). We present a curated, lexicographically ve
arXiv:2506.12090v2 Announce Type: replace Abstract: This paper introduces ChatbotManip, a novel dataset for studying manipulation in Chatbots. It contains simulated generated conversations between a c
arXiv:2510.11233v3 Announce Type: replace Abstract: Depression is a pressing global public health issue, yet publicly available Chinese-language resources for depression risk detection remain scarce a
arXiv:2605.10163v1 Announce Type: cross Abstract: Recent work on causal abstraction, in particular graphical approaches focusing on causal structure between clusters of variables, aims to summarize a
arXiv:2605.08885v1 Announce Type: new Abstract: SO(3) equivariant graph neural networks have become the dominant paradigm for atomistic foundation models, achieving high accuracy and data efficiency b
arXiv:2512.00175v3 Announce Type: replace-cross Abstract: Identifying causal effects in the presence of unmeasured variables is a fundamental challenge in causal inference, for which proxy variable me
arXiv:2605.08261v1 Announce Type: cross Abstract: Evaluating Computer Use Agents (CUAs) on interactive environments is fraught with methodological pitfalls that the field has yet to systematically add
arXiv:2601.21164v3 Announce Type: replace Abstract: Plane Geometry Problem Solving (PGPS) is a multimodal reasoning task that aims to solve a plane geometric problem based on a geometric diagram and p
arXiv:2605.10533v1 Announce Type: new Abstract: In causal inference, confounders are variables that influence both treatment decisions and outcomes. However, unlike as in randomized clinical trials, t
arXiv:2511.09493v2 Announce Type: replace Abstract: Motivated by undetectable risks in generative AI, we study a general robust aggregation problem: how to aggregate several probability distributions
arXiv:2605.09968v1 Announce Type: new Abstract: Every adaptive learning system must alternate between two operations: consolidating what it already knows and expanding into new evidence. We propose Co
arXiv:2605.08681v1 Announce Type: cross Abstract: We study solving large-scale fixed-point equation (x^star=ar F(x^star)) with decomposition. Standard strict decomposition assigns each agent a disjoin
arXiv:2605.08103v1 Announce Type: cross Abstract: High-entropy alloys (HEAs) have attracted growing attention for their exceptional mechanical and thermal properties arising from complex atomic config
arXiv:2605.08793v1 Announce Type: cross Abstract: Optimal transport (OT) has emerged as a fundamental tool in modern machine learning, yet its computational cost remains a significant bottleneck for l
arXiv:2509.25646v2 Announce Type: replace Abstract: Learning operators from data is central to scientific machine learning. While DeepONets are widely used for their ability to handle complex domains,
arXiv:2605.08396v1 Announce Type: new Abstract: The increasing complexity of modern computational environments often burdens researchers with infrastructure management, authentication protocols, and c
arXiv:2605.09291v1 Announce Type: new Abstract: Discrete flow models (DFMs) are a class of flexible generative models for generating discrete data, and diffusion large language models (dLLMs) can be v
arXiv:2605.10015v1 Announce Type: cross Abstract: In this paper, we study the problem of sampling from a distribution under the constraint of differential privacy (DP). Prior works measure the utility
arXiv:2605.08199v1 Announce Type: cross Abstract: Cardiovascular disease remains the leading cause of death globally, underscoring the need for effective, accessible monitoring solutions, particularly
arXiv:2605.09820v1 Announce Type: new Abstract: Diffusion language models (DLMs) have recently emerged as a promising alternative to autoregressive models, primarily due to their ability to enable par
arXiv:2506.11578v4 Announce Type: replace Abstract: Recently, large language models (LLMs) have demonstrated strong performance, ranging from simple to complex tasks. However, while large models achie
arXiv:2410.01656v2 Announce Type: replace-cross Abstract: We study the estimation of distributional parameters when samples are shown only if they fall in some unknown set S subseteq R^d. Kontonis, Tz
arXiv:2605.10054v1 Announce Type: new Abstract: Deep neural networks for medical image diagnosis often achieve high predictive accuracy while relying on spurious or clinically irrelevant visual cues,
arXiv:2604.06720v2 Announce Type: replace Abstract: We present DeSOPE, a large-scale dataset for 6DoF deformed objects. Most 6D object pose methods assume rigid or articulated objects, an assumption t
arXiv:2605.08198v1 Announce Type: cross Abstract: We present FairHealth, an open-source Python library that provides a unified, modular framework for trustworthy machine learning in healthcare applica
arXiv:2605.10127v1 Announce Type: new Abstract: Recent research work on fashion outfit generation focuses on promoting visual consistency of garments by leveraging key information from reference image
Vercel has introduced a fast mode option for Claude Opus 4.7 on its AI Gateway service, enabling faster response times for users integrating the model through their platform. This feature likely provi
arXiv:2605.09214v1 Announce Type: cross Abstract: Kullback-Leibler (KL) regularization is ubiquitous in reinforcement learning algorithms in the form of reverse or forward KL. Recent studies have demo
arXiv:2605.08925v1 Announce Type: new Abstract: Interactive segmentation allows efficient label generation by leveraging user-provided clicks to progressively refine predictions, which is critical whe
arXiv:2502.18834v2 Announce Type: replace-cross Abstract: Financial time series (FinTS) record the behavior of human-brain-augmented decision-making, capturing valuable historical information that can
arXiv:2605.10792v1 Announce Type: cross Abstract: We propose an implicit neural formulation of optimal transport that eliminates adversarial min--max optimization and multi-network architectures commo
arXiv:2605.09147v1 Announce Type: cross Abstract: Part-of-speech (POS) tagging for Medieval Romance languages remains challenging due to orthographic variation, morphological complexity, and limited a
arXiv:2605.08178v1 Announce Type: cross Abstract: Federated Graph Learning (FGL) enables collaborative learning over distributed graph data, yet existing approaches largely rely on a closed-world assu
arXiv:2605.10645v1 Announce Type: new Abstract: Data-driven medical AI is traditionally formulated as a discriminative mapping from input X to output Y via a learned function f, which does not general